normal shift
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2020 ◽  
Vol 8 (3) ◽  
pp. 385-406 ◽  
Author(s):  
Brett Fiebiger

As is well known, the closure of the canonical Neo-Kaleckian model is an endogenous rate of capacity utilisation. To allay concerns of Harrodian instability one response has been to endogenise the normal rate to effective demand pressures. Recent contributions have stressed microfoundations for an adjustment in the normal rate towards the actual rate. The new approach focuses on shiftwork and redefines capacity utilisation as the average workweek of capital. This paper examines whether the new concept of capacity utilisation can provide a firmer basis for endogeneity in the normal rate. It argues that the assumption of variability in the normal shift system cannot be generalised across manufacturing industries, while the potential relevance for non-manufacturing industries is unknown. Another concern is that long-run trends in the average workweek of capital and aggregate demand do not coincide. The paper also finds that the long-run trend in the US Federal Reserve's index of capacity utilisation for the manufacturing sector is not flat as frequently claimed. Instead, there is a downward trend from the mid 1960s, which matches the slowdown in aggregate demand.


ICR Journal ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 124-126
Author(s):  
Shahino Mah Abdulllah

The adoption of AI in many industries has been regarded by some as a threat to low- and middle-skill workers, as it will drastically cut down reliance on the human workforce. Besides unemployment, there are also concerns about rising economic inequality caused by AI-driven companies. With fewer employees, these companies could gain a disproportionate advantage over conventional companies that still depend on normal, shift-based systems. There is also the issue that some AI bots have achieved the capability to interact with humans and build relationships through conversations. This influential communication could eventually enable these bots to affect human behaviour and possibly trigger certain actions. Significantly, therefore, such intelligent machines are not immune to mistakes and confusion since not all possible examples of real world interaction are covered during their training; this weakness could be manipulated to fulfil certain ends. Also, Al cannot be expected to be entirely fair and neutral, since it is dependent on human programmers, who have their own interests and whims.


2014 ◽  
Vol 35 (11) ◽  
pp. 1411-1413 ◽  
Author(s):  
Mallory A. Boutin ◽  
Kerri A. Thom ◽  
Min Zhan ◽  
J. Kristie Johnson

Healthcare worker attire may become contaminated with pathogenic organisms during a normal shift. We performed a randomized crossover study to assess whether treatment with an antimicrobial coating would decrease bacterial contamination on scrubs. Thirty percent of all scrubs were contaminated; there was no difference in the rate of contamination between the intervention and control groupsInfect Control Hosp Epidemiol 2014;35(11):1411–1413


2002 ◽  
Vol 30 (9) ◽  
pp. 541-557
Author(s):  
Ruslan A. Sharipov

One of the ways of transforming hypersurfaces in Riemannian manifold is to move their points along some lines. In Bonnet construction of geodesic normal shift, these points move along geodesic lines. Normality of shift means that moving hypersurface keeps orthogonality to the trajectories of all its points. Geodesic lines correspond to the motion of free particles if the points of hypersurface are treated as physical entities obeying Newton's second law. An attempt to introduce some external forceFacting on the points of moving hypersurface in Bonnet construction leads to the theory of dynamical systems admitting a normal shift. As appears in this theory, the force fieldFof dynamical system should satisfy some system of partial differential equations. Recently, this system of equations was integrated, and explicit formula forFwas obtained. But this formula is local. The main goal of this paper is to reveal global geometric structures associated with local expressions forFgiven by explicit formula.


1995 ◽  
Vol 103 (2) ◽  
pp. 543-549
Author(s):  
A. Yu. Boldin ◽  
V. V. Dmitrieva ◽  
S. S. Safin ◽  
R. A. Sharipov

1994 ◽  
Vol 100 (2) ◽  
pp. 997-1000
Author(s):  
A. Yu. Boldin ◽  
R. A. Sharipov

Biometrics ◽  
1972 ◽  
Vol 28 (2) ◽  
pp. 639
Author(s):  
R. C. Milton
Keyword(s):  

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